{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "82fd799a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>站点名称</th>\n",
       "      <th>POI编号</th>\n",
       "      <th>拼音名称</th>\n",
       "      <th>gd经度</th>\n",
       "      <th>gd纬度</th>\n",
       "      <th>路线名称</th>\n",
       "      <th>城市名称</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>金安桥</td>\n",
       "      <td>BV11494889</td>\n",
       "      <td>JinAn Qiao</td>\n",
       "      <td>116.162586</td>\n",
       "      <td>39.923298</td>\n",
       "      <td>S1线</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>四道桥</td>\n",
       "      <td>BV10813547</td>\n",
       "      <td>si dao qiao</td>\n",
       "      <td>116.134010</td>\n",
       "      <td>39.916030</td>\n",
       "      <td>S1线</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>桥户营</td>\n",
       "      <td>BV10813541</td>\n",
       "      <td>qiao hu ying</td>\n",
       "      <td>116.125809</td>\n",
       "      <td>39.912383</td>\n",
       "      <td>S1线</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>上岸</td>\n",
       "      <td>BV10813543</td>\n",
       "      <td>shang an</td>\n",
       "      <td>116.122225</td>\n",
       "      <td>39.905138</td>\n",
       "      <td>S1线</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>栗园庄</td>\n",
       "      <td>BV10813544</td>\n",
       "      <td>li yuan zhuang</td>\n",
       "      <td>116.123254</td>\n",
       "      <td>39.895780</td>\n",
       "      <td>S1线</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5029</th>\n",
       "      <td>锡林公园</td>\n",
       "      <td>BV10875474</td>\n",
       "      <td>XiLin GongYuan</td>\n",
       "      <td>111.687460</td>\n",
       "      <td>40.770883</td>\n",
       "      <td>2号线</td>\n",
       "      <td>呼和浩特</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5030</th>\n",
       "      <td>内大南校区</td>\n",
       "      <td>BV10875440</td>\n",
       "      <td>NeiDaNan XiaoQu</td>\n",
       "      <td>111.692463</td>\n",
       "      <td>40.759146</td>\n",
       "      <td>2号线</td>\n",
       "      <td>呼和浩特</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5031</th>\n",
       "      <td>帅家营</td>\n",
       "      <td>BV10875453</td>\n",
       "      <td>ShuaiJiaYing</td>\n",
       "      <td>111.706981</td>\n",
       "      <td>40.755604</td>\n",
       "      <td>2号线</td>\n",
       "      <td>呼和浩特</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5032</th>\n",
       "      <td>喇嘛营</td>\n",
       "      <td>BV10875441</td>\n",
       "      <td>LaMaYing</td>\n",
       "      <td>111.731336</td>\n",
       "      <td>40.755684</td>\n",
       "      <td>2号线</td>\n",
       "      <td>呼和浩特</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5033</th>\n",
       "      <td>阿尔山路</td>\n",
       "      <td>BV10875438</td>\n",
       "      <td>AErShan Lu</td>\n",
       "      <td>111.747816</td>\n",
       "      <td>40.755465</td>\n",
       "      <td>2号线</td>\n",
       "      <td>呼和浩特</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5034 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       站点名称       POI编号             拼音名称        gd经度       gd纬度 路线名称  城市名称\n",
       "0       金安桥  BV11494889       JinAn Qiao  116.162586  39.923298  S1线    北京\n",
       "1       四道桥  BV10813547      si dao qiao  116.134010  39.916030  S1线    北京\n",
       "2       桥户营  BV10813541     qiao hu ying  116.125809  39.912383  S1线    北京\n",
       "3        上岸  BV10813543         shang an  116.122225  39.905138  S1线    北京\n",
       "4       栗园庄  BV10813544   li yuan zhuang  116.123254  39.895780  S1线    北京\n",
       "...     ...         ...              ...         ...        ...  ...   ...\n",
       "5029   锡林公园  BV10875474   XiLin GongYuan  111.687460  40.770883  2号线  呼和浩特\n",
       "5030  内大南校区  BV10875440  NeiDaNan XiaoQu  111.692463  40.759146  2号线  呼和浩特\n",
       "5031    帅家营  BV10875453     ShuaiJiaYing  111.706981  40.755604  2号线  呼和浩特\n",
       "5032    喇嘛营  BV10875441         LaMaYing  111.731336  40.755684  2号线  呼和浩特\n",
       "5033   阿尔山路  BV10875438       AErShan Lu  111.747816  40.755465  2号线  呼和浩特\n",
       "\n",
       "[5034 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df=pd.read_csv('metro.csv',encoding='gbk',sep=',')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e95bcc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'city': Index(['上海', '北京', '成都', '广州', '深圳', '武汉', '杭州', '重庆', '南京', '西安'], dtype='object'),\n",
       " 'num': array([535, 413, 356, 284, 284, 243, 225, 218, 174, 166], dtype=int64)}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#打出排名前十的城市\n",
    "df_city_metro_num = df['城市名称'].value_counts()\n",
    "dict_city_metro_num = {'city':df_city_metro_num.index[0:10],'num':df_city_metro_num.values[0:10]}\n",
    "dict_city_metro_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e840edb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>北京</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>成都</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>广州</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>深圳</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>武汉</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>杭州</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>重庆</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>南京</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>西安</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city  num\n",
       "0   上海  535\n",
       "1   北京  413\n",
       "2   成都  356\n",
       "3   广州  284\n",
       "4   深圳  284\n",
       "5   武汉  243\n",
       "6   杭州  225\n",
       "7   重庆  218\n",
       "8   南京  174\n",
       "9   西安  166"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#形成表格\n",
    "df_city_metro_num = df['城市名称'].value_counts()\n",
    "dict_city_metro_num = {'city':df_city_metro_num.index[0:10],'num':df_city_metro_num.values[0:10]}\n",
    "dict_city_10 = pd.DataFrame(dict_city_metro_num)\n",
    "dict_city_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81102989",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 4000x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8),dpi=200)\n",
    "x_label = df_city_metro_num.index[0:10]\n",
    "y_label = df_city_metro_num.values[0:10]\n",
    "plt.bar(x_label,y_label)\n",
    "plt.rcParams['font.sans-serif']=['simhei'] #显示中文\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dab16866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8),dpi=200)\n",
    "x_label = df_city_metro_num.index[0:10]\n",
    "y_label = df_city_metro_num.values[0:10]\n",
    "plt.bar(x_label,y_label,color='purple')\n",
    "plt.bar(x_label,y_label)\n",
    "plt.rcParams['font.sans-serif']=['simhei'] #显示中文\n",
    "plt.rcParams['axes.unicode_minus']=False  #显示中文\n",
    "plt.xlabel(\"城市\")\n",
    "plt.ylabel(\"地铁站数量\")\n",
    "plt.title(\"全球地铁站点数量排名前10的城市\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8b0de942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8),dpi=200)\n",
    "x_label = df_city_metro_num.index[0:10]\n",
    "y_label = df_city_metro_num.values[0:10]\n",
    "plt.bar(x_label,y_label,color='purple')\n",
    "for x,y in zip(x_label,y_label):\n",
    "    plt.text(x,y,'%.2f'%y,ha='center',va='bottom')\n",
    "plt.bar(x_label,y_label)\n",
    "plt.rcParams['font.sans-serif']=['simhei'] #显示中文\n",
    "plt.rcParams['axes.unicode_minus']=False  #显示中文\n",
    "plt.xlabel(\"城市\")\n",
    "plt.ylabel(\"地铁站数量\")\n",
    "plt.title(\"全球地铁站点数量排名前10的城市\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4c29c727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "城市名称  路线名称\n",
       "上海    11号线    68\n",
       "      10号线    67\n",
       "      9号线     35\n",
       "      7号线     33\n",
       "      12号线    32\n",
       "              ..\n",
       "香港    西铁线     12\n",
       "      东涌线      8\n",
       "      南港岛线     5\n",
       "      机场快线     5\n",
       "      迪士尼      2\n",
       "Name: 路线名称, Length: 232, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_lines = df.groupby(\"城市名称\")['路线名称'].value_counts()\n",
    "city_lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "565f550d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'上海': 19,\n",
       " '东莞': 1,\n",
       " '乌鲁木齐': 1,\n",
       " '佛山': 1,\n",
       " '兰州': 1,\n",
       " '北京': 24,\n",
       " '南京': 10,\n",
       " '南宁': 4,\n",
       " '南昌': 3,\n",
       " '厦门': 2,\n",
       " '合肥': 4,\n",
       " '呼和浩特': 2,\n",
       " '哈尔滨': 2,\n",
       " '大连': 7,\n",
       " '天津': 6,\n",
       " '太原': 1,\n",
       " '宁波': 4,\n",
       " '常州': 1,\n",
       " '广州': 16,\n",
       " '徐州': 2,\n",
       " '成都': 12,\n",
       " '无锡': 3,\n",
       " '昆明': 6,\n",
       " '杭州': 7,\n",
       " '武汉': 10,\n",
       " '沈阳': 4,\n",
       " '济南': 3,\n",
       " '深圳': 10,\n",
       " '温州': 1,\n",
       " '石家庄': 3,\n",
       " '福州': 2,\n",
       " '苏州': 5,\n",
       " '西安': 8,\n",
       " '贵阳': 2,\n",
       " '郑州': 7,\n",
       " '重庆': 10,\n",
       " '长春': 5,\n",
       " '长沙': 6,\n",
       " '青岛': 6,\n",
       " '香港': 11}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_city_lines = {}\n",
    "for i in range(len(city_lines)):\n",
    "    dict_city_lines[city_lines.index[i][0]]=dict_city_lines.get(city_lines.index[i][0],0)+1\n",
    "dict_city_lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "04afd5e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('北京', 24),\n",
       " ('上海', 19),\n",
       " ('广州', 16),\n",
       " ('成都', 12),\n",
       " ('香港', 11),\n",
       " ('南京', 10),\n",
       " ('武汉', 10),\n",
       " ('深圳', 10),\n",
       " ('重庆', 10),\n",
       " ('西安', 8),\n",
       " ('大连', 7),\n",
       " ('杭州', 7),\n",
       " ('郑州', 7),\n",
       " ('天津', 6),\n",
       " ('昆明', 6),\n",
       " ('长沙', 6),\n",
       " ('青岛', 6),\n",
       " ('苏州', 5),\n",
       " ('长春', 5),\n",
       " ('南宁', 4),\n",
       " ('合肥', 4),\n",
       " ('宁波', 4),\n",
       " ('沈阳', 4),\n",
       " ('南昌', 3),\n",
       " ('无锡', 3),\n",
       " ('济南', 3),\n",
       " ('石家庄', 3),\n",
       " ('厦门', 2),\n",
       " ('呼和浩特', 2),\n",
       " ('哈尔滨', 2),\n",
       " ('徐州', 2),\n",
       " ('福州', 2),\n",
       " ('贵阳', 2),\n",
       " ('东莞', 1),\n",
       " ('乌鲁木齐', 1),\n",
       " ('佛山', 1),\n",
       " ('兰州', 1),\n",
       " ('太原', 1),\n",
       " ('常州', 1),\n",
       " ('温州', 1)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_city_lines = {}\n",
    "for i in range(len(city_lines)):\n",
    "    dict_city_lines[city_lines.index[i][0]]=dict_city_lines.get(city_lines.index[i][0],0)+1\n",
    "dict_city_sort = sorted(dict_city_lines.items(),key=lambda x:x[1], reverse=True)  # 让其降序排列\n",
    "dict_city_sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3af9d019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[24, 19, 16, 12, 11, 10, 10, 10, 10, 8]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_city_lines = {}\n",
    "for i in range(len(city_lines)):\n",
    "    dict_city_lines[city_lines.index[i][0]]=dict_city_lines.get(city_lines.index[i][0],0)+1\n",
    "dict_city_sort = sorted(dict_city_lines.items(),key=lambda x:x[1], reverse=True)  # 让其降序排列\n",
    "cities = [dict_city_sort[i][0] for i in range(10)]\n",
    "num = [dict_city_sort[i][1] for i in range(10)]\n",
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f3cad884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8),dpi=200)\n",
    "x_label = cities\n",
    "y_label = num\n",
    "plt.plot(x_label,y_label,color='purple')  # 把bar改成plot就是折线图\n",
    "for x,y in zip(x_label,y_label):\n",
    "    plt.text(x,y,'%.2f'%y,ha='center',va='bottom')\n",
    "plt.bar(x_label,y_label)\n",
    "plt.rcParams['font.sans-serif']=['simhei'] #显示中文\n",
    "plt.rcParams['axes.unicode_minus']=False  #显示中文\n",
    "plt.xlabel(\"城市\")\n",
    "plt.ylabel(\"地铁站数量\")\n",
    "plt.title(\"全球地铁站点数量排名前10的城市\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "101c1fba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8111331f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
